Aiming to show a complete workthrough of 10X Spatial Gene Expression data from raw reads to analysis and visualization using 10X genomics spaceranger and Seurat.
Tutorial adapted from 10X spaceranger tutorial and Seurat vignette
The data is publicly available Mouse Brain Coronal Section
https://www.10xgenomics.com/resources/datasets/mouse-brain-section-coronal-1-standard-1-1-0
curl https://s3-us-west-2.amazonaws.com/10x.files/samples/spatial-exp/1.1.0/V1_Adult_Mouse_Brain/V1_Adult_Mouse_Brain_fastqs.tar -o datasets/V1_Adult_Mouse_Brain_fastqs.tar
curl https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Adult_Mouse_Brain/V1_Adult_Mouse_Brain_image.tif -o datasets/V1_Adult_Mouse_Brain_image.tif
# Download mouse reference
curl -O https://cf.10xgenomics.com/supp/spatial-exp/refdata-gex-mm10-2020-A.tar.gz
Space ranger was used with the following params:
./spaceranger-2.0.0/spaceranger count --id="V1_Adult_Mouse_Brain" \
--description="Adult Mouse Brain (Coronal)" \
--transcriptome=refdata-gex-mm10-2020-A \
--fastqs=datasets/V1_Adult_Mouse_Brain_fastqs \
--image=datasets/V1_Adult_Mouse_Brain_image.tif \
--slide=V19L01-041 \
--area=C1 \
--jobmode=local \
--localcores=8 \
--localmem=40
library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
We are loading in the data directly from the output of spaceranger
Make sure hd5f libraries are installed
#brain <-
brain <- Load10X_Spatial(data.dir ="outs")
plot1 <- VlnPlot(brain, features = "nCount_Spatial", pt.size = 0.1) + NoLegend()
plot2 <- SpatialFeaturePlot(brain, features = "nCount_Spatial") + theme(legend.position = "right")
wrap_plots(plot1, plot2)
The plots show quite a wide variance in molecular counts. This is due to technical variation but also due to natural variation in tissue types across the section. For this reason LogNormalise() is not suitable and Seurat reccomend rthe use of SCTransform() instead
brain <- SCTransform(brain, assay = "Spatial", verbose = FALSE)
Highlight particular features
SpatialFeaturePlot(brain, features = c("Hpca", "Ttr"))
p1 <- SpatialFeaturePlot(brain, features = "Ttr", pt.size.factor = 1)
p2 <- SpatialFeaturePlot(brain, features = "Ttr", alpha = c(0.1, 1))
p1 + p2
brain <- RunPCA(brain, assay = "SCT", verbose = FALSE)
brain <- FindNeighbors(brain, reduction = "pca", dims = 1:30)
brain <- FindClusters(brain, verbose = FALSE)
brain <- RunUMAP(brain, reduction = "pca", dims = 1:30)
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p1 <- DimPlot(brain, reduction = "umap", label = TRUE)
p2 <- SpatialDimPlot(brain, label = TRUE, label.size = 3)
p1 + p2
SpatialDimPlot(brain, cells.highlight = CellsByIdentities(object = brain, idents = c(2, 1, 4, 3,
5, 8)), facet.highlight = TRUE, ncol = 3)
SpatialDimPlot(brain, interactive = TRUE)
SpatialFeaturePlot(brain, features = "Ttr", interactive = TRUE)
1st method : Using prior knowledge
de_markers <- FindMarkers(brain, ident.1 = 5, ident.2 = 6)
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SpatialFeaturePlot(object = brain, features = rownames(de_markers)[1:3], alpha = c(0.1, 1), ncol = 3)
2nd method: Looking for patterns in the data
brain <- FindSpatiallyVariableFeatures(brain, assay = "SCT", features = VariableFeatures(brain)[1:500],
selection.method = "markvariogram")
More tutorials on data integration, subsetting and multiple slice data available at https://satijalab.org/seurat/articles/spatial_vignette.html